Time variance and defect prediction in software projects: Towards an exploitation of periods of stability and change as well as a notion of concept drift in software projects

It is crucial for a software manager to know whether or not one can rely on a bug prediction model. A wrong prediction of the number or the location of future bugs can lead to problems in the achievement of a project's goals. In this paper we first verify the existence of variability in a bug prediction model's accuracy over time both visually and statistically. Furthermore, we explore the reasons for such a high variability over time, which includes periods of stability and variability of prediction quality, and formulate a decision procedure for evaluating prediction models before applying them. To exemplify our findings we use data from four open source projects and empirically identify various project features that influence the defect prediction quality. Specifically, we observed that a change in the number of authors editing a file and the number of defects fixed by them influence the prediction quality. Finally, we introduce an approach to estimate the accuracy of prediction models that helps a project manager decide when to rely on a prediction model. Our findings suggest that one should be aware of the periods of stability and variability of prediction quality and should use approaches such as ours to assess their models' accuracy in advanc

A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation

Intrinsic isometric shape matching has become the standard approach for pose invariant correspondence estimation among deformable shapes. Most existing approaches assume global consistency, i.e., the metric structure of the whole manifold must not change significantly. While global isometric matching is well understood, only a few heuristic solutions are known for partial matching. Partial matching is particularly important for robustness to topological noise (incomplete data and contacts), which is a common problem in real-world 3D scanner data. In this paper, we introduce a new approach to partial, intrinsic isometric matching. Our method is based on the observation that isometries are fully determined by purely local information: a map of a single point and its tangent space fixes an isometry for both global and the partial maps. From this idea, we develop a new representation for partial isometric maps based on equivalence classes of correspondences between pairs of points and their tangent spaces. From this, we derive a local propagation algorithm that find such mappings efficiently. In contrast to previous heuristics based on RANSAC or expectation maximization, our method is based on a simple and sound theoretical model and fully deterministic. We apply our approach to register partial point clouds and compare it to the state-of-the-art methods, where we obtain significant improvements over global methods for real-world data and stronger guarantees than previous heuristic partial matching algorithms.Comment: 17 pages, 12 figure

Using Machine Learning and Graph Mining Approaches to Improve Software Requirements Quality: An Empirical Investigation

Software development is prone to software faults due to the involvement of multiple stakeholders especially during the fuzzy phases (requirements and design). Software inspections are commonly used in industry to detect and fix problems in requirements and design artifacts, thereby mitigating the fault propagation to later phases where the same faults are harder to find and fix. The output of an inspection process is list of faults that are present in software requirements specification document (SRS). The artifact author must manually read through the reviews and differentiate between true-faults and false-positives before fixing the faults. The first goal of this research is to automate the detection of useful vs. non-useful reviews. Next, post-inspection, requirements author has to manually extract key problematic topics from useful reviews that can be mapped to individual requirements in an SRS to identify fault-prone requirements. The second goal of this research is to automate this mapping by employing Key phrase extraction (KPE) algorithms and semantic analysis (SA) approaches to identify fault-prone requirements. During fault-fixations, the author has to manually verify the requirements that could have been impacted by a fix. The third goal of my research is to assist the authors post-inspection to handle change impact analysis (CIA) during fault fixation using NL processing with semantic analysis and mining solutions from graph theory. The selection of quality inspectors during inspections is pertinent to be able to carry out post-inspection tasks accurately. The fourth goal of this research is to identify skilled inspectors using various classification and feature selection approaches. The dissertation has led to the development of automated solution that can identify useful reviews, help identify skilled inspectors, extract most prominent topics/keyphrases from fault logs; and help RE author during the fault-fixation post inspection

To Preference via Entrenchment

We introduce a simple generalization of Gardenfors and Makinson's epistemic entrenchment called partial entrenchment. We show that preferential inference can be generated as the sceptical counterpart of an inference mechanism defined directly on partial entrenchment.Comment: 16 page

Loupe: Verifying Publish-Subscribe Architectures with a Magnifying Lens

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Context2Name: A Deep Learning-Based Approach to Infer Natural Variable Names from Usage Contexts

Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occupies less space, but also makes the code extremely difficult to manually inspect and understand. This paper presents Context2Name, a deep learningbased technique that partially reverses the effect of minification by predicting natural identifier names for minified names. The core idea is to predict from the usage context of a variable a name that captures the meaning of the variable. The approach combines a lightweight, token-based static analysis with an auto-encoder neural network that summarizes usage contexts and a recurrent neural network that predict natural names for a given usage context. We evaluate Context2Name with a large corpus of real-world JavaScript code and show that it successfully predicts 47.5% of all minified identifiers while taking only 2.9 milliseconds on average to predict a name. A comparison with the state-of-the-art tools JSNice and JSNaughty shows that our approach performs comparably in terms of accuracy while improving in terms of efficiency. Moreover, Context2Name complements the state-of-the-art by predicting 5.3% additional identifiers that are missed by both existing tools

Searching for a new center: U.S. securities markets in transition

Technological challenges, governance issues, competitive pressures, and questions about the oversight of trading practices are but a few of the many forces besetting U.S. equity markets. This article outlines some important issues surrounding the evolving structure of the U.S. equity markets and offers some alternative regulatory approaches that might be more consistent with this new competitive environment. ; Crafted more than a quarter-century ago in a time of dominant markets interacting with smaller competitors, the National Market System (NMS) was based on a one-size-fits-all approach in which all trade orders were to be treated equally. Over time, technology has enabled the development of new trading systems and dramatically expanded the number of potential competitors. In addition, the traditional market structure of member-owned cooperatives has given way to corporate ownership, which poses challenges for the self-regulatory structure underlying equity market supervision. ; Within this new environment, how should markets compete, how should they be linked, and how should they be regulated? To address these questions, the author discusses several pressing issues such as price-time priority, liquidity rebates, tape revenue, pricing increments, and access fees as well as more general issues such as the viability of self-regulation. ; The Securities and Exchange Commission’s proposed Regulation NMS is a promising start toward addressing the current market structure’s needs. But the author feels that its changes are piecemeal and do not go deep enough to develop a new vision consistent with the current economic realities of equity markets. Where direction is most needed, she concludes, is at the firm level, where regulation must.